check_residuals {insurancerating}

Check model residuals

Description

Detect overall deviations from the expected distribution.

Usage

check_residuals(object, n_simulations = 30)

Arguments

object

a model object

n_simulations

number of simulations (defaults to 30)

Details

Misspecifications in GLMs cannot reliably be diagnosed with standard residual plots, and GLMs are thus often not as thoroughly checked as LMs. One reason why GLMs residuals are harder to interpret is that the expected distribution of the data changes with the fitted values. As a result, standard residual plots, when interpreted in the same way as for linear models, seem to show all kind of problems, such as non-normality, heteroscedasticity, even if the model is correctly specified. check_residuals() aims at solving these problems by creating readily interpretable residuals for GLMs that are standardized to values between 0 and 1, and that can be interpreted as intuitively as residuals for the linear model. This is achieved by a simulation-based approach, similar to the Bayesian p-value or the parametric bootstrap, that transforms the residuals to a standardized scale. This explanation is adopted from DHARMa::simulateResiduals().

Value

Invisibly returns the p-value of the test statistics. A p-value < 0.05 indicates a significant deviation from expected distribution.

Author(s)

Martin Haringa

References

Dunn, K. P., and Smyth, G. K. (1996). Randomized quantile residuals. Journal of Computational and Graphical Statistics 5, 1-10.

Gelman, A. & Hill, J. Data analysis using regression and multilevel/hierarchical models Cambridge University Press, 2006

Hartig, F. (2020). DHARMa: Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models. R package version 0.3.0. https://CRAN.R-project.org/package=DHARMa

Examples

## Not run: 
m1 <- glm(nclaims ~ area, offset = log(exposure), family = poisson(),
data = MTPL2)
check_residuals(m1, n_simulations = 50) |> autoplot()

## End(Not run)